How is your team handling fuzzy duplicates, outliers, and missing values in Fabric?

[SachiP]

We’re building a tool to help automate the worst parts of real-world data cleaning — especially for teams working in Fabric and Power BI.
Common headaches we hear from data teams:

• Fuzzy duplicates across merged sources (different spellings, casing, etc.)

• Outliers that skew dashboards and break models

• Missing values that kill calculated columns or ML prep

We’ve built patterns to automate:

• Dynamic outlier detection (beyond simple Z-scores)

• Smart missing value imputation (context-aware)

• Fuzzy matching + deduplication across joins

👉Curious: How is your team currently solving these?
Is it mostly manual, or are you using any automated tools?

Would love to hear what’s working — or what’s still painful.

@FabricPlatformForums

[SachiP]

great Thanks! How much time does it take for you to clean a dataset?

[v-pgoloju · Accepted solution]

Hi @SachiP,

As per my knowledge, I've been using a manual process to handle fuzzy duplicates, outliers, and missing values in Power BI

Thanks & Regards,

Prasanna Kumar